發(fā)布時間：2018-06-12 06:58

  本文選題：學習焦慮 + 情感識別��； 參考：《西南大學》2017年碩士論文

【摘要】：在心理學和教育學領(lǐng)域的研究中,學習焦慮一直受到廣大學者和研究人員的重視,但是也存在很多的問題。目前,還不能夠準確定量的反映出個體是否存在學習焦慮情緒,大多是通過量表和生理檢測手段對學習焦慮狀態(tài)進行定性的刻畫,缺乏能夠定量分析的可靠方法和技術(shù)手段。同時,對學習焦慮缺乏有效的實時監(jiān)測與調(diào)節(jié)策略改善,這使得調(diào)節(jié)者不能準確掌握個體的適應度、接受度,這些都極大的降低了情感調(diào)節(jié)的可操作性。由于生理信號的客觀真實性,基于生理信號的學習焦慮識別成為了情感計算領(lǐng)域的重要研究方向。本文主要是利用設(shè)計的一套實驗方案來采集被試的皮膚電信號,并針對皮膚電的學習焦慮識別提出了一種改進的離散二進制粒子群算法做特征選擇,并使用BP神經(jīng)網(wǎng)絡(luò)做學習焦慮的識別;在情感調(diào)節(jié)方面,參考Gross的情感調(diào)節(jié)模型,提出了人機交互環(huán)境下學習焦慮的調(diào)節(jié)模型,最后根據(jù)研究的理論成果設(shè)計和開發(fā)了基于Android的學習焦慮識別與調(diào)節(jié)助手。具體工作如下:(1)實驗數(shù)據(jù)采集和特征提取。根據(jù)采集設(shè)備Shimmer3 GSR的特點以及情感誘發(fā)方案設(shè)計了一個采集學習焦慮的GSR信號的實驗,實驗設(shè)置兩組實驗場景,一組實驗場景為模擬的外語課堂環(huán)境,該組主要是采集被試的學習焦慮的10min數(shù)據(jù);另一組為觀看輕松、緩和的視頻的環(huán)境,該組主要是采集被試正常狀態(tài)下的10min數(shù)據(jù)。接下來進行數(shù)據(jù)預處理,即根據(jù)被試在實驗過程中反應,截取了20S被試在學習焦慮狀態(tài)下和非學習焦慮狀態(tài)下的信號,實驗過程中,總共有43個被試參加,經(jīng)篩選,最終有35個被試的實驗數(shù)據(jù)合格,即35個學習焦慮的實驗樣本數(shù)據(jù)以及35個非學習焦慮的樣本數(shù)據(jù)。因此本文實驗中產(chǎn)生了70個原始樣本數(shù)據(jù)。然后對原始樣本進行小波去噪以作為新的樣本,并提取每個樣本的30個時頻域統(tǒng)計特征。(2)特征組合優(yōu)化與學習焦慮的識別。主要是建立特征優(yōu)化模型和分類器模型。在特征組合優(yōu)化過程中,本文采用了離散二進制粒子群優(yōu)化算法(PSO),并從增強粒子多樣性、提高收斂速度以及跳出局部最優(yōu)等方面改進了離散二進制粒子群算法;在學習焦慮的識別過程中,采用了BP神經(jīng)網(wǎng)絡(luò)作為識別模型,并在基礎(chǔ)上確定了特征優(yōu)化的適應度目標函數(shù)。最終給出了特征組合優(yōu)化結(jié)果和學習焦慮的識別結(jié)果。實驗結(jié)果表明改進的粒子群算法選擇的最優(yōu)特征子集在BP神經(jīng)網(wǎng)絡(luò)中收斂效果要好、識別率較高。(3)建立學習焦慮的調(diào)節(jié)模型。建立了基于Gross情感調(diào)節(jié)模型的人機交互下的學習焦慮調(diào)節(jié)模型,這種調(diào)節(jié)模型能從環(huán)境控制、注意力改變、用戶認知重評、用戶能力與表達抑制等方面綜合考慮并對學習焦慮情緒進行調(diào)節(jié)。(4)基于Android的學習焦慮識別與調(diào)節(jié)助手的設(shè)計與實現(xiàn)。即利用便攜式GSR采集設(shè)備,實時采集用戶的GSR數(shù)據(jù)到手機上,并根據(jù)前面關(guān)于學習的識別與調(diào)節(jié)理論,設(shè)計和實現(xiàn)一個App用于實時監(jiān)控用戶學習焦慮狀態(tài),如果存在學習焦慮情緒就對用戶進行調(diào)節(jié)。
[Abstract]:In the field of psychology and education, learning anxiety has been paid much attention by many scholars and researchers, but there are many problems. At present, it is not able to accurately quantify whether the individual has learning anxiety or not, most of which are qualitatively depicted by the scale and physiological test. There is a lack of reliable and technical methods for quantitative analysis. At the same time, the lack of effective real-time monitoring and adjustment strategies for learning anxiety makes the regulators fail to accurately grasp the adaptability and acceptability of the individual, which greatly reduce the maneuverability of emotional adjustment. The recognition of learning anxiety has become an important research direction in the field of emotional computing. This paper mainly uses a set of experimental scheme designed to collect the skin electrical signals of the subjects, and proposes an improved discrete binary particle swarm optimization algorithm for the learning anxiety recognition of skin skin, and uses the BP neural network to do the learning focus. In the aspect of emotion regulation, referring to the emotion regulation model of Gross, the adjustment model of learning anxiety under the human-computer interaction environment is put forward. Finally, according to the theoretical results of the research, the Android based learning anxiety identification and adjustment assistant are designed and developed. The specific work is as follows: (1) experimental data acquisition and feature extraction. The characteristics of Shimmer3 GSR and the emotional induction scheme designed a GSR signal for learning anxiety. The experiment set two sets of experimental scenes, one set of experimental scenes as a simulated foreign language classroom environment, and the group was mainly the 10min data collecting the learning anxiety of the subjects; the other group was the environment for watching the relaxed and relaxed video. If we collect the 10min data in the normal state of the subjects, then the data preprocessing, that is, according to the reaction in the experiment, intercepts the signals under the learning anxiety state and the non learning anxiety state of the subjects. In the experiment, there are 43 participants in the experiment. After screening, the experimental data of the 35 subjects are qualified, that is, 35. The experiment sample data of learning anxiety and the sample data of 35 non learning anxiety. Therefore, 70 original sample data are produced in this experiment. Then, the original sample is denoised by wavelet denoising as a new sample, and the statistical characteristics of 30 time and frequency domain of each sample are extracted. (2) the identification of feature combination optimization and learning anxiety. In the process of feature combination optimization, the discrete binary particle swarm optimization (PSO) is adopted in this paper, and the discrete binary particle swarm optimization (PSO) is improved from the enhancement of particle diversity, the speed of convergence and the best jump out of the local optimality. In the process of learning anxiety, the BP God is used. On the basis of the network as the recognition model, the fitness target function is determined on the basis of the feature optimization. Finally, the results of feature combination optimization and learning anxiety are given. The experimental results show that the optimal subset selection of the improved particle swarm optimization algorithm is better in the BP neural network, and the recognition rate is higher. (3) the establishment of learning is established. The adjustment model of anxiety is established. A learning anxiety regulation model based on Gross emotion regulation model is established. This model can be considered from environmental control, attention change, user's cognitive reassessment, user ability and expression inhibition. (4) learning anxiety based on Android The design and implementation of the adjustment assistant: using the portable GSR acquisition equipment, collecting the user's GSR data on the mobile phone in real time, and designing and implementing a App to monitor the user's learning anxiety in real time according to the recognition and adjustment theory of the previous learning, and adjust the user if there is a learning anxiety.
【學位授予單位】：西南大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：G442;TN911.7;TP18

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